Table REF  demonstrates the quantitative comparison with our baselines and the state-of-the-art methods. It can be observed from Table REF  that our proposed model outperforms the existing PointNet [1]}, PointNet++ [2]}, ECC [3]}, and RTN [4]} methods for segmented parts. The reason for improvements can be observed visually in Figure 2, where segmented results produced by our model are very close to the ground-truths. Further, we also compare our results qualitatively with PointNet [1]} in Figure 3. One can note that our segmented part results are better than this method (see the marked area). Having achieved the desired results on segmentation using our proposed approach, we now validate our method for classification.
